Dependability and performability analysis
In this tutorial, we discuss several practical issues regarding specification and solution of dependability and performability models. We compare model types with and without rewards. Continuous-time Markov chains (CTMCs) are compared with (continuous-time) Markov reward models (MRMs) and generalized stochastic Petri nets (GSPNs) are compared with stochastic reward nets (SRNs). It is shown that reward-based models could lead to more concise model specification and solution of a variety of new measures. With respect to the solution of dependability and performability models, we identify three practical issues: largeness, stiffness, and non-exponentiality, and we discuss a variety of approaches to deal with them, including some of the latest research efforts.
Duke Scholars
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- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
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Published In
DOI
EISSN
ISSN
Publication Date
Volume
Start / End Page
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences